ARTICLE

We are releasing the code and data corresponding to our ArXiv pre-print on weakly-supervised 3D shape completion — a follow-up work on our earlier CVPR’18 paper. The article provides links to the GitHub repositories and data downloads as well as detailed descriptions. It also highlights the differences between the two papers.

In this pre-print, we improve upon our earlier CVPR'18 work on weakly-supervised 3D shape completion on ShapeNet [], KITTI [] and ModelNet []. In particular, we achieve higher-quality predictions and also present additional experiments as well as improved benchmarks.

Efficient C++ implementation for voxelizing watertight triangular meshes into occupancy grids and/or signed distance functions (SDFs). This tool was used to create the shape completion benchmarks as described below.

This is a Python implementation of TSDF Fusion similar to [] using and ; this approach was used to obtain simplified and watertight meshes for our synthetic benchmarks.

Data

In our paper, we created three novel shape completion benchmarks: based on ShapeNet [], KITTI [] and ModelNet10 []. Here, we provide the data for the shape completion benchmark of cars derived from ShapeNet and KITTI. The corresponding download links can be found in the repository or the table below.

Except for ModelNet10 and Kinect, all downloads include benchmarks for three difference resolutions. On ShapeNet and KITTI, these are $24\times 54\times24$, $32\times72\times32$ and $48\times108\times48$. On ModelNet, these include $32^3$, $48^3$ and $64^3$.

The "clean" and "noisy" versions of our ShapeNet benchmark; which means that we synthetically generated observations without or with noise which can be used to benchmark shape completion methods. Note that this is not the same as for our CVPR'18 paper.

Our benchmark derived from KITTI; it uses the ground truth 3D bounding boxes to extract observations from the LiDAR point clouds. It does not include ground truth shapes; however, we tried to generate an alternative by considering the same bounding boxes in different timesteps. Note that this is not the same as for our CVPR'18 paper.

ABOUTTHEAUTHOR

In September, I was honored to receive the MINT-Award IT 2018, sponsored by ZF and audimax, for my master thesis on weakly-supervised shape completion. For CVPR 2019, however, I am working on a different topic: adversarial robustness and generalization of deep neural networks.
18thOCTOBER2018 , David Stutz

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